Towards Conversational Recommendation over Multi-Type Dialogs
This work addresses the challenge of making conversational AI more natural and proactive in recommendation scenarios, though it is incremental as it builds on existing dialog and recommendation systems.
The authors tackled the problem of conversational recommendation by introducing a new task that involves bots proactively transitioning from non-recommendation to recommendation dialogs, and they created a Chinese dataset (DuRecDial) with about 10k dialogs and 156k utterances to facilitate research, establishing baseline results for future studies.
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. Dataset and codes are publicly available at https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.